• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistica neerlandica >ML– and semiparametric estimation in logistic models with incomplete covariate data
【24h】

ML– and semiparametric estimation in logistic models with incomplete covariate data

机译：不完整协变量数据的逻辑模型中的ML和半参数估计

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

ML–estimation of regression parameters with incomplete covariate information usually requires a distributional assumption regarding the concerned covariates that implies a source of misspecification. Semiparametric procedures avoid such assumptions at the expense of efficiency. In this paper a simulation study with small sample size is carried out to get an idea of the performance of the ML–estimator under misspecification and to compare it with the semiparametric procedures when the former is based on a correct assumption. The results show that there is only a little gain by correct parametric assumptions, which does not justify the possibly large bias when the assumptions are not met. Additionally, a simple modification of the complete case estimator appears to be nearly semiparametric efficient.
机译：带有不完整协变量信息的回归参数的ML估计通常需要关于相关协变量的分布假设，这暗示了错误指定的来源。半参数过程会以效率为代价避免此类假设。在本文中，进行了一个小样本量的模拟研究，以了解错误估计条件下ML估计量的性能，并在前者基于正确假设的情况下将其与半参数过程进行比较。结果表明，正确的参数假设只能带来很小的收益，当不满足这些假设时，这并不能证明可能存在较大的偏差。此外，对完整案例估算器的简单修改似乎几乎是半参数有效的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号